Sign up to receive free email alerts when patent applications with chosen keywords are publishedSIGN UP

Abstract:

A method including searching image data corresponding to a series of
axial image slices with a processor, searching axial image slices from a
starting image slice and calculating a confidence score that an image
slice includes a cross-section image of an aorta, identifying an image
slice containing at least one seed disk, including an ascending aorta
seed disk, from candidate image slices identified according to the
confidence score, and growing a 3D segmentation of the ascending aorta by
stacking ascending aorta image disks included in consecutive image slices
beginning from the ascending aorta seed disk.

Claims:

1. A method of detecting an aorta without requiring a user seed input,
the method comprising: selecting individual image slices within a series
of image slices for a candidate cross-section image of the aorta, wherein
the series of image slices is used to reconstruct a three-dimensional
(3D) image; calculating a confidence score of the candidate cross-section
image of the aorta for individual image slices within the series of image
slices; identifying an image slice, within the series of image slices,
having a highest confidence score; and growing a segmentation of the
aorta by stacking aorta image disks located in consecutive image slices.

2. The method of claim 1, wherein the candidate cross-section image of
the aorta is evaluated for the ascending aorta.

3. The method of claim 1, wherein calculating a confidence score
comprises: determining how closely the candidate cross-section image of
the aorta resembles a circle; and storing an identifier for a candidate
image slice in a candidate list if the confidence score exceeds a
threshold score value.

4. The method of claim 3, comprising identifying, from clustering, a seed
image slice in the series of image slices, including: identifying one or
more candidate clusters as a set of candidate image slices that meet an
image slice similarity criterion; and identifying, as a seed cluster, a
candidate cluster having a maximum number of candidate image slices;
wherein identifying an image slice, within the series of image slices,
having a highest confidence score comprises identifying, as the seed
image slice, an image slice having a highest confidence score within the
seed cluster.

5. The method of claim 4, wherein identifying one or more candidate
clusters includes: calculating a radial distance between neighbor
candidate disks in the candidate list, wherein the neighbor candidate
disks are located in different image slices; for individual candidate
disks in the candidate list, using the radial distance for finding N
closest neighboring candidate disks; and forming one or more clusters of
mutually neighboring candidate disks.

6. The method of claim 1, comprising: searching axial image slices from a
starting image slice corresponding to the most inferior slice in a search
region defined as a fraction of a total scan volume where a descending
aorta is likely to be found; calculating a confidence score that an image
slice includes a candidate cross-section image of a descending portion of
an aorta; identifying a seed image slice containing a descending aorta
seed disk; and identifying a seed image slice containing an ascending
aorta seed disk; wherein identifying the ascending aorta seed disk
includes: locating a descending aorta seed disk in an image slice from
candidate image slices using the confidence score; and searching
cross-section images of the descending aorta on image slices in a
superior direction from the descending aorta seed disk to locate the seed
image slice including the ascending aorta seed disk, wherein the seed
image slice also includes a descending aorta image disk.

7. The method of claim 6, comprising: restarting the search for a seed
image slice from a most superior slice in a search region if no seed
image slice containing a descending aorta seed disk is found; and when no
candidate image slices are found, abandoning a search for a descending
aorta seed disk.

8. The method of claim 6, comprising: growing a segmentation of the
descending aorta by tracking consecutive image slices in superior and
inferior directions from the descending aorta seed disk; and stacking
candidate descending aorta image disks included in the image slices.

9. The method of claim 8, wherein growing a segmentation of the
descending aorta comprises: calculating a confidence score for individual
candidate descending aorta image disks, the confidence score indicating
whether a cross-section image of the disk represents a descending aorta;
smoothing the confidence scores of the candidate descending aorta image
disks; and forming a descending aorta segmentation from the stacked
candidate descending aorta image disks; and finding superior and inferior
cut-off points for the descending aorta segmentation by truncating
candidate descending aorta image disks from one or both ends of the
descending aorta segmentation from a location at one or both ends of the
descending aorta segmentation at which the smoothed confidence scores
fall below a specified minimum descending aorta confidence score value.

10. The method of claim 9, comprising: accepting the descending aorta
segmentation when an inferior portion of the descending aorta
segmentation has an average confidence score value that exceeds the
minimum descending aorta confidence score value; restarting the search
from a starting image slice corresponding to a most superior slice in a
search region and re-growing the descending aorta segmentation when an
average confidence score value of the inferior portion of the
segmentation is less than the specified minimum descending aorta
confidence threshold value; and abandoning the descending aorta
segmentation when an average confidence score of image disks tracked in a
superior portion of the re-grown descending aorta segmentation is less
than the specified minimum descending aorta confidence threshold value.

11. The method of claim 1, comprising: searching axial image slices from
a starting image slice corresponding to the most superior slice in a
search region defined as a fraction of a total scan volume where an
ascending aorta is likely to be found; wherein calculating a confidence
score of the candidate cross-section image of the aorta comprises
calculating a confidence score that an image slice includes a
cross-section image of an ascending aorta; and wherein identifying the
image slice includes locating an ascending aorta image disk in an image
slice for use as an ascending aorta seed image from candidate image
slices using the confidence score.

12. The method of claim 1, wherein growing a segmentation of the aorta
for the ascending aorta includes: locating candidate ascending aorta
image disks in both superior and inferior directions from an ascending
aorta seed disk; choosing whether to accept a candidate ascending aorta
image disk according to a confidence score of that ascending aorta image
disk; when a limit in the superior or inferior direction has been
reached, constraining acceptance of the segmentation to the limit; and
stacking accepted candidate ascending aorta image disks to form the
segmentation until both superior and inferior candidate ascending aorta
image disks have a confidence score that is less than a minimum ascending
aorta confidence score value.

13. The method of claim 12, comprising searching consecutive image slices
in both superior and inferior directions from a starting image slice
until a specified number of candidate image slices is found or until both
superior and inferior limit image slices have been reached.

14. The method of claim 12, comprising accepting a final construction of
the ascending aorta segmentation when: the ascending aorta image
segmentation includes a minimum number of candidate ascending aorta image
disks; and an average confidence score of the accepted candidate
ascending aorta image disks exceeds the minimum ascending aorta
confidence score value.

15. The method of claim 1, wherein image data for the series of image
slices includes sub-sampled data having a fraction of a resolution of
data in an original image such that the growing a segmentation of the
aorta includes growing a rough segmentation of the aorta.

16. The method of claim 15, comprising refining the rough segmentation
into a 3D image of the aorta using higher resolution image data.

17. The method of claim 16, comprising: calculating an approximate
intensity value of an aortic wall; expanding the candidate cross-section
image of the aorta in the image slices used to grow the segmentation of
the aorta outward to include those pixels, if any, having an intensity
value that exceeds an approximate intensity value of the aortic wall; and
re-growing the 3D image of the aorta using the expanded candidate
cross-section image in the image slices.

18. A system comprising: a first memory providing a series of axial image
slices used to reconstruct a three-dimensional (3D) image; and a
processor, operably coupled to the first memory, wherein the processor
includes an automatic aortic detection module comprising: a search
module, operable to search axial image slices provided in the first
memory from a starting image slice; a seed disk detection module,
operable to: calculate a confidence score that an image slice includes a
candidate cross-section image of an aorta; identify an image slice within
the series of image slices having a highest confidence score as the image
slice including a seed disk; and an aorta segmentation module operable to
grow a segmentation of the aorta by tracking consecutive image slices
beginning from the seed disk and stacking candidate image disks included
in the image slices.

19. A non-transitory computer readable storage medium including computer
performable instructions that, when performed by a computer, detect an
aorta without requiring user seed input, by: selecting individual image
slices within a series of image slices for a candidate cross-section
image of the aorta, wherein the series of image slices is used to
reconstruct a three-dimensional (3D) image; calculating a confidence
score of the candidate cross-section image of the aorta for individual
image slices within the series of image slices; identifying an image
slice, within the series of image slices, having a highest confidence
score; and growing a segmentation of the aorta by stacking aorta image
disks located in consecutive image slices.

20. The computer readable storage medium of claim 19, wherein the
instructions are operable to: calculate a confidence score indicating how
closely the candidate cross-section image of the aorta resembles a
circle, and store an identifier for a candidate image slice in a
candidate list when the confidence score exceeds a threshold score value.

21. The computer readable storage medium of claim 19, wherein the
instructions are configured for determining: a bound to limit a search of
image data for the series of images slices to a subset of image slices
where the aorta is likely to be found; a designated starting image slice
within the subset from which to begin the search of the image data; and a
search of consecutive image slices in both superior and inferior
directions from the starting image slice, the search adding to the
candidate list until a specified number of candidate image slices are
found or until both superior and inferior limit image slices have been
reached; and wherein the instructions are operable to: identify one or
more candidate clusters as a set of similar candidate image slices using
an image slice similarity criterion; identify a candidate cluster having
a highest number of candidate image slices as a seed cluster; and
identify an image slice having a highest confidence score within the seed
cluster as the image slice including the seed disk.

22. The computer readable storage medium of claim 19, wherein the
instructions are operable to: calculate a confidence score indicating how
closely a cross-section image resembles a circle; identify a most
superior slice containing a cross-section image of a descending aorta as
a superior cut-off slice; limit a search of the data to image slices in
the inferior direction from the superior cut-off slice and limit the
search area within the image slices to where the ascending aorta is
likely to be found; locate a cross-section image of the aorta on an image
slice and calculate a confidence score that the cross-section image
represents the ascending aorta; and declare an image slice to be the
ascending aorta seed image slice based on the confidence score.

23. The computer readable storage medium of claim 19, wherein the
instructions are operable to grow a segmentation of the ascending aorta
by: locating axial image slices in both the superior and inferior
directions from the ascending aorta seed disk; choosing whether to accept
an ascending aorta image disk in the superior direction or an image disk
in the inferior direction into the ascending aorta segmentation based on
a confidence score of that ascending image disk or whether a search limit
in the superior or inferior direction has been reached; and stacking
accepted image disks to form the segmentation until both superior and
inferior disk candidates have a confidence level less than a minimum
ascending aorta confidence score value.

24. The computer readable storage medium of claim 23, wherein the
instructions are operable to declare a stack of image disks as a final
construction of the segmentation of the aorta if the stack consists of a
minimum number of aorta image disks and if the average confidence score
of the accepted image disks exceeds the minimum aorta confidence score
value.

25. The computer readable storage medium of claim 19, wherein image data
providing the series of image slices includes sub-sampled data having a
fraction of the resolution of original image data, wherein the
instructions are operable to search data that includes the sub-sampled
data, and wherein the instructions are operable to grow a rough
segmentation of the aorta using the sub-sampled data and refine the rough
segmentation of the aorta into a 3D image of the aorta using image data
having a higher resolution than the sub-sampled data.

26. The computer readable storage medium of claim 25, wherein the
instructions are operable to refine the rough segmentation of an
ascending aorta, or a rough segmentation of a descending aorta, or a
rough segmentation of both the ascending and descending aorta into a 3D
image using the image data having a higher resolution than the
sub-sampled data by: calculating an approximate intensity value of the
aortic wall; expanding the cross-section image of the ascending aorta in
the image slices used to grow the image of the ascending aorta outward to
include those pixels, if any, having an intensity value higher than the
approximate intensity value of the aortic wall; and re-growing the 3D
image of the ascending aorta using the expanded cross-section image in
the image slices.

27. The computer readable storage medium of claim 26, wherein the
instructions are operable to provide a user interface, wherein the user
interface is operable to allow a user to select between displaying the 3D
image of the ascending aorta, or the 3D image of the descending aorta, or
the 3D image of both the ascending and descending aorta.

28. The computer readable storage medium of claim 25, wherein the
instructions are operable to grow a rough segmentation of the descending
aorta from cross-section images of the descending aorta using image
slices superior and inferior to the descending aorta seed image slice.

29. The computer readable storage medium of claim 28, wherein the
instructions are operable to grow a rough segmentation of the descending
aorta by: stacking axial image disks of the descending aorta in both the
superior and inferior directions from the descending aorta seed image
slice to form the rough segmentation of the descending aorta, wherein the
descending aorta image disks are located within a volume where the
descending aorta is likely to be found; smoothing confidence scores
calculated for the descending aorta image disks; and finding superior and
inferior cut-off points for the descending aorta image by truncating
image disks from one or both ends of the descending aorta segmentation
from a point at one or both ends of the image at which the smoothed
confidence scores fall below a specified minimum descending aorta
confidence score value.

Description:

RELATED APPLICATIONS

[0001] This application is a Continuation of U.S. application Ser. No.
11/287,165, filed on Nov. 23, 2005, which is incorporated herein by
reference in its entirety.

TECHNICAL FIELD

[0002] The field generally relates to image processing and, in particular
but not by way of limitation, to systems and methods for automatically
detecting and segmenting an aorta in image data without requiring a user
seed input.

BACKGROUND

[0003] Computed X-ray tomography (CT) is a 3D viewing technique for the
diagnosis of internal diseases. FIG. 1 shows an example of a prior art CT
system 100. The system includes an X-ray source 105 and an array of X-ray
detectors 110. In CT, the X-Ray source 105 is rotated around a subject
115 by a CT scanner. The X-ray source 105 projects radiation through the
subject 115 onto the detectors 110 to collect projection data. A contrast
agent may be introduced into the blood of the subject 115 to enhance the
acquired images. The subject 115 may be placed on a movable platform 120
that is manipulated by a motor 125 and computing equipment 130. This
allows the different images to be taken at different locations. The
collected projection data is then transferred to the computing equipment
130. A 3D image is then reconstructed mathematically from the rotational
X-ray projection data using tomographic reconstruction. The 3D image can
then be viewed on the video display 135.

[0004] Magnetic Resonance Imaging (MRI) is a diagnostic 3D viewing
technique where the subject is placed in a powerful uniform magnetic
field. In order to image different sections of the subject, three
orthogonal magnetic gradients are applied in this uniform magnetic field.
Radio frequency (RF) pulses are applied to a specific section to cause
hydrogen atoms in the section to absorb the RF energy and begin
resonating. The location of these sections is determined by the strength
of the different gradients and the frequency of the RF pulse. After the
RF pulse has been delivered, the hydrogen atoms stop resonating, release
the absorbed energy, and become realigned to the uniform magnetic field.
The released energy can be detected as an RF pulse. Because the detected
RF pulse signal depends on specific properties of tissue in a section,
MRI is able to measure and reconstruct a 3D image of the subject. This 3D
image or volume consists of volume elements, or voxels.

[0005] Image segmentation refers to extracting data pertaining to one or
more meaningful structures or regions of interest (i.e., "segmented
data") from imaging data that includes other data that does not pertain
to such one or more structures or regions of interest (i.e.,
"non-segmented data.") As an illustrative example, a cardiologist may be
interested in viewing only 3D image of a certain portion of the aorta.
However, the raw image data typically includes the aorta along with the
nearby heart and other thoracic tissue, bone structures, etc. Image
segmentation can be used to provide enhanced visualization and
quantification for better diagnosis. The present inventors have
recognized a need in the art for improvements in 3D data segmentation and
display, such as to improve speed, accuracy, and/or ease of use for
diagnostic or other purposes.

SUMMARY

[0006] This document discusses, among other things, systems and methods
for automatically detecting and segmenting an aorta without requiring a
user input, such as a user-specified seed location. A system example
includes a first memory to store image data corresponding to a series of
axial image slices that are used to reconstruct a three-dimensional (3D)
image and a processor in communication with the first memory. The
processor includes an automatic aortic detection module that includes a
search module to search consecutive axial image slices stored in the
first memory from a starting image slice, a seed disk detection module to
calculate a confidence score that an image slice includes a cross-section
image of an aorta and to identify an image slice containing at least one
seed disk from candidate image slices identified according to the
confidence score, and an aorta segmentation module to grow a segmentation
of the ascending portion of the aorta by stacking ascending aorta image
disks included in consecutive image slices beginning from the ascending
aorta seed disk.

[0007] A method example includes searching image data corresponding to a
series of axial image slices with a processor, searching the axial image
slices from a starting image slice and calculating a confidence score
that an image slice includes a cross-section image of an aorta,
identifying an image slice containing at least one seed disk from
candidate image slices identified according to the confidence score, and
growing a segmentation of the ascending aorta by stacking ascending aorta
image disks included in consecutive image slices beginning from the
ascending aorta seed disk.

[0008] This summary is intended to provide an overview of the subject
matter of the present patent application. It is not intended to provide
an exclusive or exhaustive explanation of the invention. The detailed
description is included to provide further information about the subject
matter of the present patent application.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIG. 1 is an illustration of an example of a CT system.

[0010] FIG. 2 shows a block diagram of an example of a method of
automatically detecting an ascending aorta from the image data.

[0011] FIG. 3 shows a representation of an aorta and a dashed outline of
the heart.

[0012] FIG. 4 shows a cross section taken from an image slice including a
cross-section of a descending aorta.

[0013] FIGS. 5-7 show examples of cross-section images taken from image
slices.

[0014] FIG. 8 is a block diagram of portions of a system that
automatically detects an ascending aorta without requiring a user seed
input.

DETAILED DESCRIPTION

[0015] In the following detailed description, reference is made to the
accompanying drawings which form a part hereof, and specific examples in
which the invention may be practiced are shown by way of illustration. It
is to be understood that other embodiments may be used and structural or
logical changes may be made without departing from the scope of the
present invention.

[0016] The functions or methods described herein can be implemented in
software. The software comprises computer executable, interpretable, or
otherwise performable instructions stored on computer readable media such
as memory or other type of storage devices. The term "computer readable
media" is also used to represent carrier waves on which the software is
transmitted. Further, such functions can be implemented in modules, which
can be software, hardware, firmware or any combination thereof. Multiple
functions can be performed in one or more modules as desired, and the
embodiments described are merely examples. The software is typically
executed on a processor operating on a computer system, such as a
personal computer, workstation, server, or other computer system.

[0017] This document discusses, among other things, systems and methods
for automatically detecting and segmenting an ascending and descending
aorta without requiring a user input, such as a user-specified seed
location. The systems and methods are described in terms of extracting
image segments from image data obtained using X-ray computed tomography
(CT) images, but the methods and systems described herein also can be
used to extract image segments from image data created by other
techniques, such as MRI.

[0018] To collect image data, a CT imaging system is manipulated to
collect a series of axial images from a subject. The axial images are
actually three-dimensional images and are referred to as image slices.
The series of image slices comprise a scan volume, such as a scan of the
chest volume of the subject for example. These image slices have a
thickness that depends on the accuracy of the CT imaging system. The
image slices can be reconstructed into a three-dimensional (3D) image
volume of the subject.

[0019] FIG. 2 shows a block diagram of an example of a method 200 of
automatically detecting an ascending aorta from the image data, without
requiring a user seed input.

[0020] This is in contrast to a segmentation that is created after a user
provides a starting point in the image data from which to begin the
segmentation, such as by clicking a mouse at a point in 3D volume where
the user deems that the ascending portion of the aorta exists. Such user
seed input can be referred to as a "one click" segmentation method. The
embodiments described herein provide a computer implemented method to
automatically locate image data corresponding to an ascending aorta and
create the segmentation without user seed or similar input, and can
therefore be conceptualized as a "no click" segmentation method.

[0021] At 210, image data corresponding to a series of axial image slices
is searched with a controller or other processor, which typically
operates by executing instructions in software, or firmware, or a
combination of software and firmware. In some embodiments the image data
is stored in memory of the computer system in communication with the
processor. In some embodiments, the image data is stored on a server and
the processor loads the image data over a network into the computer
system. In some examples, the image data is sub-sampled data, i.e., data
that is sampled at less than full resolution of the CT system. This
allows the image data to be searched more quickly to find meaningful
structures than by searching full resolution image data. Typically, the
sub-sampled data is a fraction of the highest resolution data. In some
examples, the image data is one-half of the highest resolution available.
The highest resolution of image data acquired by a CT system is sometimes
referred to as RR1 data. Image data at one-half the resolution is
sometimes referred to as RR2 data. In some examples, the image data is
one-fourth of the resolution of the RR1 data, or RR4 data. In some
examples, the stored image data includes a combination of high resolution
and lower resolution data. In some examples, the stored image data
includes three full sets of image data; corresponding to each of the
three resolutions, RR1, RR2, and RR4.

[0022] At 220, axial image slices are searched from a starting image slice
and a confidence score is calculated. If RR4 image data is searched, the
search will be accomplished fairly quickly. Non-consecutive image slice
searching may be useful if the image data includes a large number of
image slices having a small thickness.

[0023] The goal of the search is to locate a cross-section image of the
ascending portion of the aorta. The confidence score is a measure of
confidence that the searched image slices include such a cross-section.
If a cross-section image of the ascending aorta is found and there is
sufficient confidence that the cross-section image is indeed of the
ascending aorta, a 3D ascending aorta image seed disk corresponding to
the two-dimensional (2D) cross-section image is designated and used to
create the ascending aorta segmentation.

[0024] In some examples, however, the ascending aorta is instead found by
first finding a cross-section image of the descending portion of the
aorta. This is illustrated in FIG. 3. FIG. 3 shows a representation of an
aorta 300 and a dashed outline of the heart 305. The ascending aorta 310
and the descending aorta 315 are also shown. A cross-section image of the
descending aorta is typically easier to find in image slices in the
inferior portion of the descending aorta 315. This corresponds to image
slices at or near position 320. To more quickly find an image slice that
contains the descending aorta, the search volume is limited to a subset
of image slices. In some examples, the search is begun at a starting
image slice corresponding to the most inferior slice in a search region
defined as a fraction of a total scan volume where an aorta is likely to
be found. In an illustrative example, the search is also bounded to image
slices corresponding to the posterior two-thirds of a chest scan volume.
Image slices are then searched in the superior direction from the
starting image slice.

[0025] As the image slices are searched, a confidence score is calculated
to first detect the descending aorta cross-section image. The confidence
score is typically calculated by detecting 2D objects in the image
slices. In some examples, the confidence score is calculated by
determining how closely the cross-section image of the aorta resembles a
circle. FIG. 4 shows a cross section 400 taken from an image slice. A
cross-section image of the descending aorta 410 is located in the bottom
half of FIG. 4 and it can be seen that the cross-section 410 resembles a
circle. The Hough transform is useful for detecting objects in images. In
some examples, the Hough transform is used to detect circle-like objects
in the image slices and provide a confidence score for the image slices
as they are tracked. If the confidence score for an image slice exceeds a
specified threshold score value, the image slice becomes a candidate for
containing the desired cross-section image; here, an image of a
cross-section of a descending portion of an aorta.

[0026] Once a desired cross-section of a portion of the descending aorta
315 is identified, such as at location 320 in FIG. 3, image slices are
then searched in the superior direction for a cross-section image of an
ascending aorta 310. A confidence score is calculated to detect a 2D
ascending aorta cross-section image in such superior image slices. As the
image slice search approaches image slices near position 325, the image
slices will include both a cross-section image of the descending aorta
and a cross-section image of the ascending aorta. An example of a
cross-section image 500 near position 325 is shown in FIG. 5. In the
example of FIG. 5, the circular cross-section of the descending aorta 510
can be seen as well as a less circular cross-section of the ascending
aorta 520.

[0027] FIG. 6 shows an example of a cross-section image 600 in an image
slice near position 330 in FIG. 3. In this image slice, the cross-section
images of both the descending aorta 610 and the ascending aorta 620
resemble circles. Thus, in some examples, another confidence score is
calculated by determining how closely the cross-section image of the
ascending aorta resembles a circle. To create a segmentation of the
ascending aorta, a seed disk for the segmentation is found. The
segmentation is then "grown" from the location of the seed disk. An
ascending aorta image disk comprises the thickness of the image slice
circumscribed by the cross-section image of the ascending aorta 620. An
image disk formed from the ascending aorta cross-section 620 would be a
candidate for an ascending aorta seed disk, from which an aortic
segmentation can be generated.

[0028] Returning to FIG. 2, at 230 an image slice is identified as
containing the ascending aorta seed disk, such as by using the confidence
score. Until such an ascending aorta seed disk is found, a confidence
score is calculated across an entire image slice, which adds time to the
search. Then, at 240, a segmentation of the ascending aorta is grown,
such as by tracking consecutive image slices beginning from the ascending
aorta seed disk and stacking the ascending aorta image disks included in
the image slices. Such "tracking" searches consecutive image slices, but
only looks within a reduced area of the image slices to locate image
disks, based on where the image disks were found in an adjacent image
slice. Because it is known where in the image slices that the region
including the desired image disks will be found, the amount of search
time is reduced as compared to looking across an entire image slice.

[0029] As an illustrative example, if the ascending aorta seed disk is
located at position 330 in FIG. 3, the aortic segmentation is grown in
the superior and inferior directions from the ascending aorta seed disk.
In some examples, a descending aorta seed disk is also identified. This
is useful to limit the number of slices where the ascending aorta may be
found. It also is useful to grow a segmentation of the descending aorta
if desired. The image slice shown in FIG. 4 includes a descending aorta
image disk, and the image slice shown in FIG. 5 includes both a
descending aorta image disk and an ascending aorta image disk. In some
examples, the search for a descending aorta seed disk is abandoned if no
candidate image slices are found.

[0030] In searching for an ascending aorta seed disk, it is helpful to
further limit the search to a subset of image slices where such an
ascending aorta seed disk is likely to be found. If a heart segmentation
was previously computed, the search can be limited using image slices
corresponding to the heart segmentation. In some examples, if no heart
segmentation was computed, the search can be limited to image slices
corresponding to the anterior two-thirds of the scan volume. In some
examples, if the descending aorta has already been identified, an initial
radius search range is defined as rmin≦r≦rmax,
where rmin is the maximum descending aorta radius computed over all
valid image slices, i.e., those image slices having a confidence score
greater than a specified threshold score value, and rmax is 30
millimeters (mm). In certain examples, the intensities of voxels within
the search volume are clamped intensity values in the range of between
-100 Hounsfield units (HU) and +400 HU. In a typical cardiac scan, the
ascending portion of the aorta resides somewhere in the superior half (or
less) of the scan volume, depending on the field of view. For image data
at RR4 resolution, this could mean there are only a handful of image
slices (often as few as five) where the cross-section of the ascending
aorta actually resembles a circle.

[0031] If the descending aorta is found, the search typically begins at
the superior cutoff slice of the descending aorta. In FIG. 3, this
superior cutoff slice will be located near position 335. An example 700
of a cross-section image near the position 335 is shown in FIG. 7. The
cross-section includes a cross-section image of the descending aorta 710
and the ascending aorta 720. It can be seen that, at this location, the
descending aorta cross-section no longer resembles a circle. In some
examples, the superior cutoff slice of the descending aorta is detected
from the decreasing confidence score as image slices are searched from
the more circular cross-section image slices near positions 320, 325 to
the superior position at 335. To expedite the procedure, the search can
be confined to that part of the image slices where the descending aorta
is more likely to be found. The ascending aorta seed disk is found by
searching image slices in the inferior direction from the superior cutoff
slice of the descending aorta.

[0032] If the descending aorta was not found (or moves out of the frame of
the scan as image slices are searched), the search for the ascending
aorta begins at the most superior axial image slice in the search region
or volume. The search is confined to a subset of images slices where the
ascending aorta is likely to be found. In some examples, the search is
further confined to the area of the image slices where the ascending
aorta is likely to be found (such as based on its location in an adjacent
image slice). A confidence score that an image slice includes a
cross-section image of an ascending aorta is calculated. Then, the
ascending aorta seed disk is identified from candidate image slices using
the confidence score (e.g., most indicative of a circle). In some
examples, if a valid seed disk for the ascending aorta is not found
within the constrained search volume, the ascending aorta segmentation is
abandoned.

[0033] Automatically identifying a seed disk (for either the ascending or
descending aorta) from image slices can be achieved by clustering. When a
candidate image slice is found while searching image slices, an
identifier for the candidate image slice is stored in memory to create a
candidate list. In some examples, the confidence score for the disk is
stored in the candidate list as well. The list can be either in the same
memory as the stored image data or in a separate memory, such as
processor memory, for example, if the processor memory does not store the
image data. This process is repeated on consecutive image slices until a
specified number of candidate image slices is found or until both
inferior and superior limit slices have been reached. Clusters of
candidate image slices are then identified from the candidate list, such
as described below.

[0034] In certain examples, one or more clusters of candidate image slices
are identified as a set of candidate image slices that are similar to
each other based on an image slice similarity criterion. The similarity
criterion is used to find a set of shared N-neighbor clusters, where N is
an integer, such as two, for example. In some examples, the similarity
criterion is a calculated radial distance between image disks in neighbor
candidate slices in the candidate list. These image disks can be referred
to as candidate disks. The radial distance refers to the distance between
axes and also to the radii of candidate disks. Candidate disks having
axes that are close together are grouped into clusters. If a candidate
disk is defined by (cx, cy, cz, r) where cx, cy,
cz, are the x, y, z coordinates of the candidate disk center and r
is the radial distance from the center of the disk to the edge of the
disk, then the radial distance between candidate disks c1, c2
is calculated by

where sx and sy are voxel scaling coefficients that convert
voxel distances to millimeter distances, sr=max(sx, sy),
and Dmax is the maximum neighbor distance. The radial distance for
each candidate disk in the candidate list is calculated. N neighboring
image disks in the candidate list that are within a lowest radial
distance to the candidate disk are found. Clusters are then formed of
mutually neighboring candidate disks.

[0035] After the clusters of candidate image disks are identified, a
candidate cluster having a maximum number of candidate image slices is
identified as the seed cluster. An image slice having a highest
confidence score within the seed cluster is then identified as the image
slice containing the seed disk.

[0036] As discussed above, once the ascending aorta seed disk is
identified, a segmentation of the ascending aorta is grown by tracking
and stacking ascending aorta image disks included in consecutive image
slices, for example, in both the superior and inferior directions from
the ascending aorta seed disk.

[0037] In some examples, once an ascending aorta image disk in either
direction is located, it is determined whether to accept the disk into
the segmentation based on a confidence score of the image disk, or
whether a limit in the superior or inferior direction has been reached,
or both. In some examples, searching image data for image disks includes
searching consecutive image slices in both superior and inferior
directions from a starting image slice until a specified number of
candidate image slices is found or until both superior and inferior limit
image slices have been reached. Accepted ascending aorta image disks are
stacked to form the ascending aorta segmentation until both superior and
inferior disk candidates have a confidence level less than a minimum
ascending aorta confidence score value. As an illustrative example, FIG.
6 shows a cross-section of an ascending aorta 620 that is a candidate for
an ascending aorta seed disk. The cross-section 620 corresponds to an
ascending aorta image disk at position 330 in FIG. 3. Image disks are
stacked in the superior and inferior directions from the ascending aorta
seed disk toward an image disk at position 335 near the superior cutoff
of the ascending aorta and toward an image disk at position 325 near the
aortic valve. Cross-sections 720 in FIGS. 7 and 520 in FIG. 5 at these
positions, 335, 325 respectively, show that such image disks will have a
low circular-similarity-based confidence score.

[0038] In some examples, the resulting construction of the ascending aorta
segmentation is not accepted unless the stack includes at least a minimum
number of ascending aorta image disks and the average confidence score of
the accepted ascending aorta image disks exceeds the minimum ascending
aorta confidence score value.

[0039] In some examples, the method includes also growing a segmentation
of the descending aorta. A descending aorta seed disk is identified, such
as by any of the methods discussed previously. Beginning with the
descending aorta seed image disk, descending aorta image disks are
located in consecutive image slices, in the superior and inferior
directions from the descending aorta seed image disk, and are stacked to
form the descending aorta segmentation. A circular-similarity-based
confidence score for each descending aorta image disk is calculated. The
confidence score indicates whether a cross-section image of the disk
represents a descending aorta. In certain examples, the confidence scores
are then smoothed to avoid premature cutoff of the segmentation due to an
isolated "bad" image slice that receives a low confidence score. In some
examples, the confidence scores are smoothed using Gaussian smoothing. A
location on the resulting descending aorta segmentation is found at one
or both ends at which the smoothed confidence scores fall below a
specified minimum descending aorta confidence score threshold value. The
descending aorta segmentation is truncated at such locations to define
superior and inferior cut-off points of the segmentation.

[0040] In some examples, after the descending aorta segmentation is grown,
it is determined whether to accept the resulting descending aorta
segmentation. In certain examples, the descending aorta segmentation is
accepted if an inferior half of the segmentation has an average
confidence score value that exceeds a minimum descending aorta confidence
score threshold value. If the average confidence score value of the
inferior half of the segmentation is less than the minimum descending
aorta confidence threshold value, then the search is restarted from a
starting image slice corresponding to the most superior slice in a search
region or volume. The segmentation of the descending aorta is then
re-grown. If the average confidence score of image disks tracked in a
superior half of the re-grown segmentation is less than the minimum
descending aorta confidence threshold value, then the segmentation of the
descending aorta is abandoned. In some examples, such abandonment results
if a descending aorta image seed disk is not found after searching
beginning with the inferior image slice and also searching beginning with
the most superior image slice.

[0041] In the preceding method examples, if the image data searched is
sub-sampled data (such as RR4 for example), and the segmentation is grown
using the sub-sampled data, the resulting segmentation will be a rough
segmentation. Therefore, in some examples, the rough segmentation of an
ascending aorta is refined into a finer 3D image of the ascending aorta
using image data having a higher resolution than the search data. For
example, if the image is grown with RR4 data, the image is refined using
RR2 or RR1 data. If the image is grown with RR2 data, the image is
refined using RR1 data.

[0042] Because of the lower resolution, the rough segmentation of an
ascending aorta may have to be grown out to its true boundary, and it may
be desirable to include the structural characteristics of the aortic
valve. To accomplish this, an approximate intensity value of the aortic
wall can be calculated. This is done by generating a histogram of
intensity values in a cylindrical shaped region encompassing the wall of
the aorta. The cylindrical region is formed by subtracting an eroded
version of the initial aorta construction from a dilated version of the
initial aorta construction. Within the region, a histogram of intensity
values between Imin and Imax, is constructed, where Imin
is a specified fixed value, such as 200 HU for example, and Imax is
computed to be the average voxel intensity inside the aorta plus an
additional amount, such as one standard deviation of the average voxel
intensity inside the aorta. The histogram is expected to be bimodal with
one peak representing the low intensity voxels representing tissue
outside the aorta, and the other peak representing the contrast agent
administered to the subject and located inside the aorta. The optimal
separation value of intensities inside and outside the aorta is then
calculated, such as by using Otsu's threshold algorithm.

[0043] After the threshold intensity value of the aortic wall is
calculated, two-dimensional (2D) cross-section images of the ascending
aorta are expanded in the image slices of the segmentation to grow the
image of the ascending aorta outward to include those pixels, if any,
having an intensity value that exceeds the approximate intensity value of
the aortic wall threshold intensity value. The 3D image of the ascending
aorta is then re-grown using the expanded cross-section image in the
image slices. The rough segmentation is typically used as a seed for the
growth process. The result of the growth may be a superset of the desired
segmentation, possibly encompassing several potential leaks from the
aorta into nearby "bright" structures. To eliminate such leaks, a drastic
erosion of the grown image is performed followed by the elimination of
all components that are not connected to the original construction. The
image is then dilated to recover what was lost by the erosion. The result
is the refined segmentation of the ascending aorta.

[0044] FIG. 8 is a block diagram of portions of a system 800 to
automatically detect an ascending aorta without requiring any user input,
such as a user-specified seed location. In this example, the system 800
includes a memory 805 to store image data 810 and a processor 815. The
image data 810 corresponds to a series of axial image slices that are
used to reconstruct a 3D image.

[0045] The processor 815 is in communication with the memory 805 such as
by communicating over a network or by the memory 805 being included in
the processor 815. In some examples, the system 800 includes a server
having a server memory, and the memory 805 storing the image data 810 is
included in the server memory. The processor 815 accesses the image data
810 from the server over the network. The processor 815 includes
performable instructions that implement an automatic aortic detection
module 820 that in turn includes a search module 825, a seed disk
detection module 830, and an aorta segmentation module 835.

[0046] The search module 825 searches axial image slices in the memory 805
from a starting image slice. The seed disk detection module 830
calculates a confidence score that an image slice includes a
cross-section image of an aorta and identifies a seed image slice
containing an ascending aorta seed disk from candidate image slices.
Candidate image slices are identified according to the confidence score.
The seed disk detection module 830 may also identify a seed image slice
containing a descending aorta seed image disk. The aorta segmentation
module 835 grows a segmentation of the ascending aorta by stacking
ascending aorta image disks included in consecutive image slices
beginning from the ascending aorta seed disk.

[0047] In some system examples, the seed image detection module 830
calculates a confidence score that indicates how closely the
cross-section image of the aorta resembles a circle. In some examples,
the confidence score is calculated using the Hough transform. The
processor 815 then stores an identifier for a candidate image slice in a
candidate list if the processor determines that the confidence score
exceeds a threshold score value. In some examples, the processor 815 also
stores the confidence score for the candidate image slice with the
identifier. In some examples, the candidate list is stored in the same
memory 805 as the image data 810, in some examples the candidate list is
located in a separate memory.

[0048] In some examples, the search module 825 bounds the image data
search to the image slices of a region or volume where the aorta is
likely to be found. The search begins at a starting image slice within
the bounded region. Consecutive image slices are searched in both
superior and inferior directions from the starting image slice and image
slices are added to the candidate list. The search module 825 continues
the search until a specified number of candidate image slices are found
or until both superior and inferior limit image slices have been reached.
In some examples, search module 825 bounds the search for the
cross-section image to an area of the image slice where the seed disk is
likely to be found.

[0049] The seed disk detection module 830 identifies one or more clusters
of candidate slices. A candidate cluster is a set of candidate image
slices that are similar to each other based on an image slice similarity
criterion. In some examples, the similarity criterion includes
calculating a radial distance between axes of neighbor image disks within
candidate image slices. For each candidate disk in the candidate list, N
neighboring candidate disks are found within a lowest radial distance to
the candidate disk. The seed disk detection module 830 forms clusters of
mutually neighboring candidate disks. The seed disk detection module 830
then identifies a candidate cluster having the highest number of
candidate image slices as the seed cluster, and identifies an image slice
having a highest confidence score within the seed cluster as the image
slice containing the seed disk.

[0050] To identify an ascending aorta seed disk image, the processor 815
identifies a most superior image slice containing a cross-section image
of a descending aorta as a superior cut-off slice and limits the search
of the image data to a range of image slices in the inferior direction
from the superior cut-off slice and limits the search range within image
slices to where the ascending aorta is likely to be found. The processor
815 locates a cross-section image of the aorta on an image slice and
calculates a confidence score that the cross-section image represents the
ascending aorta, such as by calculating the confidence that the
cross-section image represents a circle in the limited search boundary.
The processor 815 declares an image slice to contain the ascending aorta
seed image slice based on the confidence score.

[0051] In some examples, the aorta segmentation module 835 grows a
segmentation of the ascending aorta by tracking axial image slices in
both the superior and inferior directions from the ascending aorta seed
disk and chooses whether to accept an ascending aorta image disk in the
superior or inferior directions into the ascending aorta segmentation.
The choice is based on the confidence score of that ascending image disk
or whether a search limit in the superior or inferior direction has been
reached. The aorta segmentation module 835 stacks accepted image disks to
form the ascending aorta segmentation until both superior and inferior
disk candidates have a confidence level less than a minimum ascending
aorta confidence score value. In some examples, the aorta segmentation
module declares a stack of image disks as a final construction of the
segmentation of the ascending aorta if the stack consists of a minimum
number of ascending aorta image disks and if the average confidence score
of the accepted image disks exceeds the minimum ascending aorta
confidence score value.

[0052] In some examples, the image data 810 includes sub-sampled data
having a fraction of the resolution of original image data, such as RR2
or RR4 data. The search module 825 searches image data that includes the
sub-sampled data, and the aorta segmentation module 835 grows a rough
segmentation of the ascending aorta using the sub-sampled data. The aorta
segmentation module 835 includes a segmentation refining module to refine
the rough segmentation of the ascending aorta into a 3D image of the
ascending aorta using image data having a higher resolution than the
sub-sampled data such as RR1 or RR2 data for example.

[0053] In some examples, the aorta segmentation module 835 grows a rough
segmentation of the descending aorta from cross-section images of the
descending aorta. The segmentation is grown by stacking axial descending
aorta image disks in image slices superior and inferior to a descending
aorta seed image slice found by any of the methods discussed previously.
In some examples, the aorta segmentation module 835 searches for
descending aorta image disks that are located within a volume where the
descending aorta is likely to be found.

[0054] In some examples, the aorta segmentation module 835 smoothes the
confidence scores calculated for the descending aorta image disks to
avoid any premature cutoff of the segmentation due to a slice that
contains a bad cross-section image. The aorta segmentation module 835
finds a point at one or both ends of the image segmentation at which the
smoothed confidence scores fall below a specified minimum descending
aorta confidence score value. The segmentation is truncated beyond the
point or points. The aorta segmentation module 835 identifies these
points as the superior and inferior cut-off points for the descending
aorta image.

[0055] In some examples, the segmentation refining module refines the
rough segmentation of the ascending aorta, or the rough segmentation of
the descending aorta, or the rough segmentation of both the ascending and
descending aorta into a 3D image using image data having a higher
resolution than the sub-sampled data. The segmentation refining module
calculates an approximate intensity value of the aortic wall and expands
the cross-section image of the ascending aorta in the image slices used
to grow the image of the ascending aorta outward to include those pixels,
if any, having an intensity value higher than the approximate intensity
value of the aortic wall. The segmentation refining module re-grows the
3D image of the ascending aorta using the expanded cross-section image in
the image slices. In some examples, the system 800 includes a display and
a user interface coupled to the processor 815. The user interface allows
a user to select between displaying the 3D image of the ascending aorta,
or the 3D image of the descending aorta, or the 3D image of both the
ascending and descending aorta.

[0056] The systems and methods described above improve diagnostic
capability by automatically providing a segmentation of the aorta. The
segmentation is provided without requiring a user to specify a seed point
from to begin the segmentation. This allows the segmentation to begin
upon loading of the data. The user, such as a diagnosing physician,
receives the segmentation faster and easier than if the segmentation did
not begin until user input is received. This reduces the time required in
providing the segmentation. This prevents the user from possibly waiting
while the image data is loaded and the segmentation process executes. The
systems and methods of automatic segmentation of the aorta discussed
herein can be used for or combined with automatic segmentation of other
physiologic structures of interest, such as to create automatic
segmentations of compound physiologic structures.

[0057] The accompanying drawings that form a part hereof, show by way of
illustration, and not of limitation, specific embodiments in which the
subject matter may be practiced. The embodiments illustrated are
described in sufficient detail to enable those skilled in the art to
practice the teachings disclosed herein. Other embodiments may be
utilized and derived therefrom, such that structural and logical
substitutions and changes may be made without departing from the scope of
this disclosure. This Detailed Description, therefore, is not to be taken
in a limiting sense, and the scope of various embodiments is defined only
by the appended claims, along with the full range of equivalents to which
such claims are entitled.

[0058] Such embodiments of the inventive subject matter may be referred to
herein, individually and/or collectively, by the term "invention" merely
for convenience and without intending to voluntarily limit the scope of
this application to any single invention or inventive concept if more
than one is in fact disclosed. Thus, although specific embodiments have
been illustrated and described herein, it should be appreciated that any
arrangement calculated to achieve the same purpose may be substituted for
the specific embodiments shown. This disclosure is intended to cover any
and all adaptations, or variations, or combinations of various
embodiments. Combinations of the above embodiments, and other embodiments
not specifically described herein, will be apparent to those of skill in
the art upon reviewing the above description.

[0059] The Abstract of the Disclosure is provided to comply with 37 C.F.R.
§1.72(b), requiring an abstract that will allow the reader to
quickly ascertain the nature of the technical disclosure. It is submitted
with the understanding that it will not be used to interpret or limit the
scope or meaning of the claims. In addition, in the foregoing Detailed
Description, it can be seen that various features are grouped together in
a single embodiment for the purpose of streamlining the disclosure. This
method of disclosure is not to be interpreted as reflecting an intention
that the claimed embodiments require more features than are expressly
recited in each claim. Rather, as the following claims reflect, inventive
subject matter lies in less than all features of a single disclosed
embodiment. Thus the following claims are hereby incorporated into the
Detailed Description, with each claim standing on its own.